1. Technical Field
Generally, the present invention relates to inverse treatment planning. More specifically, the present invention relates to an artificial intelligence method for guiding inverse treatment planning.
2. Description of the Related Art
Modern day radiation therapy of tumors has two goals: eradication of the tumor and avoidance of damage to healthy tissue and organs present near the tumor. It is known that a vast majority of tumors can be eradicated completely if a sufficient radiation dose is delivered to the tumor; however, complications may result from use of the necessary effective radiation dose. Most complications are due to damage to healthy tissue that surrounds the tumor or to other healthy body organs located close to the tumor. The goal of conformal radiation therapy is to confine the delivered radiation dose to only the tumor volume defined by the outer surfaces of the tumor, while minimizing the dose of radiation applied to surrounding healthy tissue or adjacent healthy organs.
Conformal radiation therapy has been traditionally approached through a range of techniques and typically uses a linear accelerator (“LINAC”) as the source of the radiation beam used to treat the tumor. The linear accelerator typically has a radiation beam source that is rotated about the patient and directs the radiation beam toward the tumor to be treated. The beam intensity of the radiation beam has a predetermined, constant beam intensity. Multileaf collimators, which have multiple leaf or finger projections that can be moved individually into and out of the path of the radiation beam, can be programmed to follow the spatial contour of the tumor as seen by the radiation beam as it passes through the tumor, or the “beam's eye view” of the tumor during the rotation of the radiation beam source, which is mounted on a rotatable gantry of the linear accelerator. The multiple leaves of the multileaf collimator form an outline of the tumor shape, as presented by the tumor volume in the direction of the path of travel of the radiation beam, and thus block the transmission of radiation to tissue disposed outside the tumor's spatial outline as presented to the radiation beam, dependent upon the beam's particular radial orientation with respect to the tumor volume.
Another approach to conformal radiation therapy involves the use of independently controlled collimator jaws that can scan a slit field across a stationary patient at the same time that a separate set of collimator jaws follows the target volume as the gantry of the linear accelerator rotates. An additional approach has been the use of attachments for LINACs that allow a slit to be scanned across the patient, the intensity of the radiation beam in the entire slit being modified as the slit is being scanned.
A further approach for conformal radiation therapy treatment has been the use of a narrow pencil beam of high energy photons, with energy that can be varied, and the beam is scanned over the tumor target volume so as to deliver the best possible radiation dose distribution in each orientation of the gantry upon which the photon beam source is mounted.
A major problem associated with such prior art methods of conformal radiation therapy are that if the tumor volume has concave borders, or surfaces, varying the spatial configuration, or contour, of the radiation beam, the therapy is only successful part of the time. In particular, when the convolutions, or outer surfaces, of a tumor are re-entrant, or concave, in a plane parallel to the path of the radiation treatment beam, healthy tissue or organs may be disposed within the concavities formed by the outer tumor concave surfaces, as well as the fact that the thickness of the tumor varies along the path of the radiation beam.
In order to be able to treat tumors having concave borders, it is necessary to vary the intensity of the radiation beam across the surface of the tumor, as well as vary the outer configuration of the beam to conform to the shape of the tumor presented to the radiation beam. The beam intensity of each radiation beam segment should be able to be modulated to have a beam intensity related to the thickness of the portion of the tumor through which the radiation beam passes. For example, where the radiation beam is to pass through a thick section of a tumor, the beam intensity should be higher than when the radiation beam passes through a thin section of the tumor.
Dedicated scanning beam therapy machines have been developed wherein beam intensity modulation can be accomplished through the use of a scanning pencil beam of high-energy photons. The beam intensity of this device is modulated by increasing the power of its electron gun generating the beam. The power increase is directed under computer control as the gun is steered around the tumor by moving the gantry upon which it is mounted and the table upon which the patient lies. The effect is one of progressively “painting” the target with the thickness, or intensity, of the paint, or radiation beam intensity, being varied by the amount of paint on the brush, or how much power is applied to the electron gun, as the electron gun moves over the tumor. Such dedicated scanning beam therapy machines, which utilize direct beam energy modulation, are expensive and quite time consuming in their use and operation, and are believed to have associated with them a significant patient liability due to concerns over the computer control of the treatment beam itself.
Other methods and apparatus for conformal radiation therapy have been developed that spatially modulate the beam intensity of a radiation beam across a volume of tissue in accordance with the thickness of the tumor in the volume of tissue by utilizing a plurality of radiation beam segments. Such methods and apparatus utilize attenuating leaves, or shutters, in a rack positioned within the radiation beam before the beam enters the patient. The tumor is exposed to radiation in slices, each slice being selectively segmented by the shutters. However, a minor disadvantage of that method and apparatus results from the fact that only two slices of tissue volume may be treated with one rotation of the gantry of the linear accelerator. Although the slices may be of arbitrary thickness, greater resolution is accomplished by selecting slices for treatment that are as thin as possible. As the thickness of the treatment slices decreases, the time it takes to treat the patient increases because more treatment slices are required in order to treat the entire tumor volume.
The foregoing methods and apparatus are designed to minimize the portion of the structures being exposed to radiation. However, because exposure to surrounding structures cannot be completely prevented, treatment plans are desired that are optimized to eradicate the tumor volume while minimizing the amounts of radiation delivered to the surrounding structures. Existing methods and apparatus for optimizing treatment plans use a computer to rate possible plans based on score functions, which simulate a physician's assessment of a treatment plan. However, existing methods and apparatus have proven to be insufficient.
Existing methods and apparatus utilize a computational method of establishing optimized treatment plans based on an objective cost function that attributes costs of radiation of various portions of both the tumor and surrounding tissues, or structures. One such computational method is known in the art as simulated annealing. Existing simulated annealing methods utilize cost functions that consider the costs of under-exposure of tumor volumes relative to over-exposure of surrounding structures. However, the cost functions used in existing methods do not account for the structure volumes as a whole, relying merely on costs related to discrete points within the structure, and further do not account for the relative importance of varying surrounding structure types. For example, certain structure types are redundant in their function and substantial portions of the structure volume can be completely eradicated while retaining their function. Other structure types lose their function if any of the structure is completely eradicated. Therefore, the more sensitive structure volumes can receive a measured dose of radiation so long as no portion of the structure is subjected to a lethal dose.
Existing cost functions utilized in the optimization of treatment plans do not account for such varying costs associated with the different types of structures. After the treatment plan is optimized, the physician currently must evaluate each computed treatment plan for compliance with the desired treatment objective. If the computed treatment plan does not successfully meet the treatment objectives, the optimization process is repeated until a treatment plan can be computed that meets the physician's treatment objectives for both the tumor volume and the surrounding structures. Further, existing methods and apparatus do not allow the physician to utilize the familiar Cumulative Dose Volume Histogram (“CDVH”) curves in establishing the desired dose distributions.
Recent studies indicated that conformal dose distribution could be effectively achieved with the treatment technique called intensity-modulated radiation therapy (IMRT). Several promising delivery devices have also become available, such as static or dynamic MLC and tomotherapy to deliver conformal radiation dose. The basic concept of IMRT is that a dedicated delivery device with an intensity-variable modulates a uniform intensity in a traditional treatment field. However, it is still a very challenging issue in terms of how to generate an effective and optimal intensity spectrum and how to verify modulated radiation delivery. The first issue is also related to the problem of inverse treatment planning (or treatment planning optimization).
An inverse planning method describes a specific treatment planning procedure in which, differing from traditional approach, both dose and volume are given first. Then a set of modulated beams is generated through a computer-aided optimization process in order to satisfy the prescription. The process is extremely important if the shapes of the target and critical organs are complicated, especially when the target has concavity and a critical organ lies in the hollow of the concavity. Typically, inverse treatment planning for intensity modulated radiation therapy involves the selection of an objective function and method of optimization. For a given objective function, an optimal treatment plan usually requires the optimization of beam intensity elements, a prescription method, and beam number and orientation.
One of the most challenging problems in the optimization of treatment planning is how to construct a model by which the aim of radiation therapy can be fulfilled. The models that have been studied in the past can be classified as either physical or biological. There have been detailed discussions in recent literature concerning the merits and limitations of these two types of models. While biological models may be able to directly measure the clinical outcome, they still remain in the formative stages and suffer from controversy concerning the validity of the radiobiological response data used (such as, tumor control probability (TCP) and normal tissue complication probability (NTCP)). On the other hand, the physical dosimetric prescription has been well established as the clinical norm. In the traditional physical models, one optimizes an objective function that is the measure of closeness of the calculated dose distribution to the prescribed dose distribution. The crucial problem here is how to give the optimal dose value for the normal tissue so that the two objectives, delivering the desired dose to the target volume and minimizing the dose to normal tissues, can be achieved accordingly.
A quadratic model has typically being used in inverse treatment planning. The model is widely discussed and has two major limitations, no direct biological information and no minimal constraints to normal tissues. Linguistically, the purpose of radiotherapy may be stated as (a) delivering a desired tumor dose and zero dose outside the target volume; (b) delivering a high dose to the target volume and a low dose to the normal tissue. The statement (a) and (b) may be served as absolute linguistic prescription (ALP) and relative linguistic prescription (RLP), respectively. Although the ALP is ideal, it is clearly impossible to deliver due to the laws of nature. On the other hand, RLP clinically describes the strategy of radiation therapy. The words ‘high’ and ‘low’ used here are vague terms that are associated with the limitation of making precise definition. The complexity of treatment planning optimization is evident from the need to formulate some kinds of clinical goals to be optimized since there is no unique treatment plan which is clinically feasible and fulfills the two conflicting objectives: maximizing dose in the target volume while minimizing dose in normal tissues.
Recently, several researchers have paid attention to the analysis of uncertainties in radiation treatment planning optimization. The tolerance of normal tissues has been discussed. Spirou et al., developed an inverse planning algorithm with soft constraints. The method allows acceptable doses of maximum and minimum as well as dose-volume constraints to the tissues of interest.
The search for the optimal beams usually can be interpreted to be an optimization problem. Thus, the searching problem is converted to find the extremum of a given objective function. Several methods and algorithms have been investigated for inverse planning. Some examples are simulated annealing iterative approaches, as well as filtered back-projection and Fourier transformation. Although these methods are very promising, there are some aspects that can be further improved upon. The simulated annealing method may require long computation time due to the nature of random search. Most iterative approaches are parameter-dependent. The convergence and the quality of convergence may be affected by these parameters that are often determined by try-and-error. The filtered back-projection and direct Fourier transformation may have limitations on dose prescription and kernel selection. Inverse treatment planning is still at its early stage and many important aspects require be to further improved.
Additionally, Starkschall proposed an approach that removed the necessity of defining a “best” treatment plan, and incorporated the dose-volume constraints into a system to search for a feasible plan that could satisfy the constraints. If no calculated doses satisfy the treatment goal, the planner provides a guide about how the dose-volume constraints may be modified to achieve a feasible result. This approach is only applied to the conventional three-dimensional (3D) treatment planning. Wu and Mohan developed an optimization system, which employed both dose- and dose-volume-based objective functions. In the system, the optimal plan is selected by calculating the cost of the objective function, or “plan score” (the lower the score, the better the plan). Xing et al. presented a method that employed a second stage evaluation function to compute the differences between the calculated and the ideal dose volume histograms. Based on the results of the evaluation function, the weighting factors in the objective function are adjusted. The procedure minimizes both the objective and evaluation functions in a round-robin manner. Later, further improvement is achieved by using a statistical measure called preference function, which is constructed based on the empirical judgment. The problem of the selection of weighting factor still exists because it translated to the problem of how to specify the parameters in the evaluation or preference function. A similar method was also proposed by Wu et al. using a genetic algorithm to optimize the weighting factors and beam weights in the conventional 3D treatment planning. Li and Yin introduced fuzzy logic into the inverse planning system to adjust the weighting factors for normal tissue. The result was promising. However, optimizing the parameters for the target and critical organ were not included in the system. Also, the weighting factors initialized by the fuzzy functions still need to be modified by the trial-and-error approach.
It would therefore be useful to develop a method or apparatus for conformal radiation therapy, for use with a radiation beam having a predetermined, constant beam intensity for treatment of a tumor, which is simple and economical to use, has a high safety factor for patient safety, computes an optimal treatment plan to meet conflicting, pre-determined, treatment objectives of a physician, accounting for objectives in both the target tumor volume and multiple structure types, and provides the desired dose distributions for each target tumor volume and tissue and structure types.